Higher-Order Interactions Dampen Pairwise Competition in the Zebrafish Gut Microbiome

Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.

Richness and density jointly determine context dependence in bacterial interactions

Pairwise interactions are often used to predict features of complex microbial communities due to the challenge of measuring multi-species interactions in high dimensional contexts. This assumes that interactions are unaffected by community context. Here, we used synthetic bacterial communities to investigate that assumption by observing how interactions varied across contexts. Interactions were most often weakly negative and showed a phylogenetic signal among genera. Community richness and total density emerged as strong predictors of interaction strength and contributed to an attenuation of interactions as richness increased. Population level and per-capita measures of interactions both displayed such attenuation, suggesting factors beyond systematic changes in population size were involved; namely, changes to the interactions themselves. Nevertheless, pairwise interactions retained some explanatory power across contexts, provided those contexts were not substantially divergent in richness. These results suggest that understanding the emergent properties of microbial interactions can improve our ability to predict the features of microbial communities.

Master regulators of biological systems in higher dimensions

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.

Dynamic social interactions and keystone species shape the diversity and stability of mixed-species biofilms - an example from dairy isolates

Identifying interspecies interactions in mixed-species biofilms is a key challenge in microbial ecology and is of paramount importance given that interactions govern community functionality and stability. We previously reported a bacterial four-species biofilm model comprising Stenotrophomonas rhizophila, Bacillus licheniformis, Microbacterium lacticum, and Calidifontibacter indicus that were isolated from the surface of a dairy pasteuriser after cleaning and disinfection. These bacteria produced 3.13-fold more biofilm mass compared to the sum of biofilm masses in monoculture. The present study confirms that the observed community synergy results from dynamic social interactions, encompassing commensalism, exploitation, and amensalism. M. lacticum appears to be the keystone species as it increased the growth of all other species that led to the synergy in biofilm mass. Interactions among the other three species (in the absence of M. lacticum) also contributed towards the synergy in biofilm mass. Biofilm inducing effects of bacterial cell-free-supernatants were observed for some combinations, revealing the nature of the observed synergy, and addition of additional species to dual-species combinations confirmed the presence of higher-order interactions within the biofilm community. Our findings provide understanding of bacterial interactions in biofilms which can be used as an interaction-mediated approach for cultivating, engineering, and designing synthetic bacterial communities.

Mathematical models of cystic fibrosis as a systemic disease

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Microbiome Assembly in Fermented Foods

For thousands of years, humans have enjoyed the novel flavors, increased shelf-life, and nutritional benefits that microbes provide in fermented foods and beverages. Recent sequencing surveys of ferments have mapped patterns of microbial diversity across space, time, and production practices. But a mechanistic understanding of how fermented food microbiomes assemble has only recently begun to emerge. Using three foods as case studies (surface-ripened cheese, sourdough starters, and fermented vegetables), we use an ecological and evolutionary framework to identify how microbial communities assemble in ferments. By combining in situ sequencing surveys with in vitro models, we are beginning to understand how dispersal, selection, diversification, and drift generate the diversity of fermented food communities. Most food producers are unaware of the ecological processes occurring in their production environments, but the theory and models of ecology and evolution can provide new approaches for managing fermented food microbiomes, from farm to ferment.

Antibiotic-degrading resistance changes bacterial community structure via species-specific responses

Some bacterial resistance mechanisms degrade antibiotics, potentially protecting neighbouring susceptible cells from antibiotic exposure. We do not yet understand how such effects influence bacterial communities of more than two species, which are typical in nature. Here, we used experimental multispecies communities to test the effects of clinically important pOXA-48-plasmid-encoded resistance on community-level responses to antibiotics. We found that resistance in one community member reduced antibiotic inhibition of other species, but some benefitted more than others. Further experiments with supernatants and pure-culture growth assays showed the susceptible species profiting most from detoxification were those that grew best at degraded antibiotic concentrations (greater than zero, but lower than the starting concentration). This pattern was also observed on agar surfaces, and the same species also showed relatively high survival compared to most other species during the initial high-antibiotic phase. By contrast, we found no evidence of a role for higher-order interactions or horizontal plasmid transfer in community-level responses to detoxification in our experimental communities. Our findings suggest carriage of an antibiotic-degrading resistance mechanism by one species can drastically alter community-level responses to antibiotics, and the identities of the species that profit most from antibiotic detoxification are predicted by their intrinsic ability to survive and grow at changing antibiotic concentrations.

A mucin-regulated adhesin determines the spatial organization and inflammatory character of a bacterial symbiont in the vertebrate gut

In a healthy gut, microbes are often aggregated with host mucus, yet the molecular basis for this organization and its impact on intestinal health are unclear. Mucus is a viscous physical barrier separating resident microbes from epithelia, but it also provides glycan cues that regulate microbial behaviors. Here, we describe a mucin-sensing pathway in an Aeromonas symbiont of zebrafish, Aer01. In response to the mucin-associated glycan N-acetylglucosamine, a sensor kinase regulates the expression of an aggregation-promoting adhesin we named MbpA. Upon MbpA disruption, Aer01 colonizes to normal levels but is largely planktonic and more pro-inflammatory. Increasing cell surface MbpA rescues these traits. MbpA-like adhesins are common in human-associated bacteria, and the expression of an Akkermansia muciniphila MbpA-like adhesin in MbpA-deficient Aer01 restores lumenal aggregation and reverses its pro-inflammatory character. Our work demonstrates how resident bacteria use mucin glycans to modulate behaviors congruent with host health.

A novel gnotobiotic experimental system for Atlantic salmon (Salmo salar L.) reveals a microbial influence on mucosal barrier function and adipose tissue accumulation during the yolk sac stage

Gnotobiotic models have had a crucial role in studying the effect that commensal microbiota has on the health of their animal hosts. Despite their physiological and ecological diversity, teleost fishes are still underrepresented in gnotobiotic research. Moreover, a better understanding of host-microbe interactions in farmed fish has the potential to contribute to sustainable global food supply. We have developed a novel gnotobiotic experimental system that includes the derivation of fertilized eggs of farmed and wild Atlantic salmon, and gnotobiotic husbandry of fry during the yolk sac stage. We used a microscopy-based approach to estimate the barrier function of the skin mucus layer and used this measurement to select the derivation procedure that minimized adverse effects on the skin mucosa. We also used this method to demonstrate that the mucus barrier was reduced in germ-free fry when compared to fry colonized with two different bacterial communities. This alteration in the mucus barrier was preceded by an increase in the number of cells containing neutral mucosubstances in the anterior segment of the body, but without changes in the number of cells containing acidic substances in any of the other segments studied along the body axis. In addition, we showed how the microbial status of the fry temporarily affected body size and the utilization of internal yolk stores during the yolk sac stage. Finally, we showed that the presence of bacterial communities associated with the fry, as well as their composition, affected the size of adipose tissue. Fry colonized with water from a lake had a larger visceral adipose tissue depot than both conventionally raised and germ-free fry. Together, our results show that this novel gnotobiotic experimental system is a useful tool for the study of host-microbe interactions in this species of aquacultural importance.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Higher-order interactions shape microbial interactions as microbial community complexity increases

Non-pairwise interactions, or higher-order interactions (HOIs), in microbial communities have been described as significant drivers of emergent features in microbiomes. Yet, the re-organization of microbial interactions between pairwise cultures and larger communities remains largely unexplored from a molecular perspective but is central to our understanding and further manipulation of microbial communities. Here, we used a bottom-up approach to investigate microbial interaction mechanisms from pairwise cultures up to 4-species communities from a simple microbiome (Hafnia alvei, Geotrichum candidum, Pencillium camemberti and Escherichia coli). Specifically, we characterized the interaction landscape for each species combination involving E. coli by identifying E. coli's interaction-associated mutants using an RB-TnSeq-based interaction assay. We observed a deep reorganization of the interaction-associated mutants, with very few 2-species interactions conserved all the way up to a 4-species community and the emergence of multiple HOIs. We further used a quantitative genetics strategy to decipher how 2-species interactions were quantitatively conserved in higher community compositions. Epistasis-based analysis revealed that, of the interactions that are conserved at all levels of complexity, 82% follow an additive pattern. Altogether, we demonstrate the complex architecture of microbial interactions even within a simple microbiome, and provide a mechanistic and molecular explanation of HOIs.

Zebrafish: an efficient vertebrate model for understanding role of gut microbiota

Gut microbiota plays a critical role in the maintenance of host health. As a low-cost and genetically tractable vertebrate model, zebrafish have been widely used for biological research. Zebrafish and humans share some similarities in intestinal physiology and function, and this allows zebrafish to be a surrogate model for investigating the crosstalk between the gut microbiota and host. Especially, zebrafish have features such as high fecundity, external fertilization, and early optical transparency. These enable the researchers to employ the fish to address questions not easily addressed in other animal models. In this review, we described the intestine structure of zebrafish. Also, we summarized the methods of generating a gnotobiotic zebrafish model, the factors affecting its intestinal flora, and the study progress of gut microbiota functions in zebrafish. Finally, we discussed the limitations and challenges of the zebrafish model for gut microbiota studies. In summary, this review established that zebrafish is an attractive research tool to understand mechanistic insights into host-microbe interaction.

Bacterial Keystone Taxa Regulate Carbon Metabolism in the Earthworm Gut

As important ecosystem engineers in soils, earthworms strongly influence carbon cycling through their burrowing and feeding activities. Earthworms do not perform these roles in isolation, because their intestines create a special habitat favorable for complex bacterial communities. However, how the ecological functioning of these earthworm-microbe interactions regulates carbon cycling remains largely unknown. To fill this knowledge gap, we investigated the bacterial community structure and carbon metabolic activities in the intestinal contents of earthworms and compared them to those of the adjacent soils in a long-term fertilization experiment. We discovered that earthworms harbored distinct bacterial communities compared to the surrounding soil under different fertilization conditions. The bacterial diversity was significantly larger in the adjacent soils than that in the earthworm gut. Three statistically identified keystone taxa in the bacterial networks, namely, Solirubrobacterales, Ktedonobacteraceae, and Jatrophihabitans, were shared across the earthworm gut and adjacent soil. Environmental factors (pH and organic matter) and keystone taxa were important determinants of the bacterial community composition in the earthworm gut. Both PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) and FAPROTAX (Functional Annotation of Prokaryotic Taxa) predicted that carbon metabolism was significantly higher in adjacent soil than in the earthworm gut, which was consistent with the average well color development obtained by the Biolog assay. Structural equation modeling combined with correlation analysis suggested that pH, organic matter, and potential keystone taxa exhibited significant relationships with carbon metabolism. This study deepens our understanding of the mechanisms underlying keystone taxa regulating carbon cycling in the earthworm gut. IMPORTANCE The intestinal microbiome of earthworms is a crucial component of the soil microbial community and nutrient cycling processes. If we could elucidate the role of this microbiome in regulating soil carbon metabolism, we would make a crucial contribution to understanding the ecological role of these gut bacterial taxa and to promoting sustainable agricultural development. However, the ecological functioning of these earthworm-microbe interactions in regulating carbon cycling has so far not been fully investigated. In this study, we revealed, first, that the bacterial groups of Solirubrobacterales, Ktedonobacteraceae, and Jatrophihabitans were core keystone taxa across the earthworm gut and adjacent soil and, second, that the environmental factors (pH and organic carbon) and keystone taxa strongly affected the bacterial community composition and exhibited close correlations with microbial carbon metabolism. Our results provide new insights into the community assembly of the earthworm gut microbiome and the ecological importance of potential keystone taxa in regulating carbon cycling dynamics.

Coexistence in diverse communities with higher-order interactions

A central assumption in most ecological models is that the interactions in a community operate only between pairs of species. However, two species may interactively affect the growth of a focal species. Although interactions among three or more species, called higher-order interactions, have the potential to modify our theoretical understanding of coexistence, ecologists lack clear expectations for how these interactions shape community structure. Here we analytically predict and numerically confirm how the variability and strength of higher-order interactions affect species coexistence. We found that as higher-order interaction strengths became more variable across species, fewer species could coexist, echoing the behavior of pairwise models. If interspecific higher-order interactions became too harmful relative to self-regulation, coexistence in diverse communities was destabilized, but coexistence was also lost when these interactions were too weak and mutualistic higher-order effects became prevalent. This behavior depended on the functional form of the interactions as the destabilizing effects of the mutualistic higher-order interactions were ameliorated when their strength saturated with species' densities. Last, we showed that more species-rich communities structured by higher-order interactions lose species more readily than their species-poor counterparts, generalizing classic results for community stability. Our work provides needed theoretical expectations for how higher-order interactions impact species coexistence in diverse communities.

Understanding the impact of third-party species on pairwise coexistence

The persistence of virtually every single species depends on both the presence of other species and the specific environmental conditions in a given location. Because in natural settings many of these conditions are unknown, research has been centered on finding the fraction of possible conditions (probability) leading to species coexistence. The focus has been on the persistence probability of an entire multispecies community (formed of either two or more species). However, the methodological and philosophical question has always been whether we can observe the entire community and, if not, what the conditions are under which an observed subset of the community can persist as part of a larger multispecies system. Here, we derive long-term (using analytical calculations) and short-term (using simulations and experimental data) system-level indicators of the effect of third-party species on the coexistence probability of a pair (or subset) of species under unknown environmental conditions. We demonstrate that the fraction of conditions incompatible with the possible coexistence of a pair of species tends to become vanishingly small within systems of increasing numbers of species. Yet, the probability of pairwise coexistence in isolation remains approximately the expected probability of pairwise coexistence in more diverse assemblages. In addition, we found that when third-party species tend to reduce (resp. increase) the coexistence probability of a pair, they tend to exhibit slower (resp. faster) rates of competitive exclusion. Long-term and short-term effects of the remaining third-party species on all possible specific pairs in a system are not equally distributed, but these differences can be mapped and anticipated under environmental uncertainty.

It is debated whether the frequency with which two species coexist in isolation or within a single environmental context is representative of their coexistence expectation within larger multispecies systems and across different environmental conditions. Here, using analytical calculations, simulations, and experimental data, we show why and how third-party species can provide the opportunity for pairwise coexistence regardless of whether a pair of species can coexist in isolation across different environmental conditions. However, we show that this opportunity is not homogeneously granted across all pairs within the same system. We provide a framework to understand and map the long-term and short-term effects that third-party species have on the coexistence of each possible subset in a multispecies system.

Disaggregation as an interaction mechanism among intestinal bacteria

The gut microbiome contains hundreds of interacting species that together influence host health and development. The mechanisms by which intestinal microbes can interact, however, remain poorly mapped and are often modeled as spatially unstructured competitions for chemical resources. Recent imaging studies examining the zebrafish gut have shown that patterns of aggregation are central to bacterial population dynamics. In this study, we focus on bacterial species of genera Aeromonas and Enterobacter. Two zebrafish gut-derived isolates, Aeromonas ZOR0001 (AE) and Enterobacter ZOR0014 (EN), when mono-associated with the host, are highly aggregated and located primarily in the intestinal midgut. An Aeromonas isolate derived from the commensal strain, Aeromonas-MB4 (AE-MB4), differs from the parental strain in that it is composed mostly of planktonic cells localized to the anterior gut. When challenged by AE-MB4, clusters of EN rapidly fragment into non-motile, slow-growing, dispersed individual cells with overall abundance two orders of magnitude lower than the mono-association value. In the presence of a certain set of additional gut bacterial species, these effects on EN are dampened. In particular, if AE-MB4 invades an already established multi-species community, EN persists in the form of large aggregates. These observations reveal an unanticipated competition mechanism based on manipulation of bacterial spatial organization, namely dissolution of aggregates, and provide evidence that multi-species communities may facilitate stable intestinal co-existence.

Point-of-use carbon-block drinking water filters change gut microbiome of larval zebrafish

Activated carbon block (ACB) point-of-use (PoU) drinking water filters can change the bacterial composition in drinking water. Consuming ACB PoU filtered water may also influence gut microbiomes. This study uses the zebrafish model to evaluate how the ACB PoU filter affects the gut microbiomes and phenotypic responses in larvae and adulthood. An ACB PoU filter manifold system was constructed to feed larval and adult zebrafish tap and filtered water at the early and late stages of the filter operation period. Adult zebrafish gut microbiomes were not affected by exposure to water types and filter stages. Unlike the adult, gut microbiomes of the larvae exposed to filtered water at the late stage of filter operation were dominated by more filter-relevant bacterial taxa, including Comamonadaceae and Brevundimonas, than the early stage-filtered-water- and tap water-exposed larvae. We also found some fish that were either exposed to filtered water at early and late stages or tap water supplied to the filter toward the end of the experiment showed hyperactive locomotion behaviour, and had significantly lower relative abundances of a Pseudomonas spp. (OTU3) than the normally behaved fish. Our findings indicate that ACB PoU filtered water can alter gut microbiomes and affect the behaviour patterns in larval zebrafish.

Engineering complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

Early-life gut microbiota and its connection to metabolic health in children: Perspective on ecological drivers and need for quantitative approach

The colonisation and development of the gut microbiota has been implicated in paediatric metabolic disorders via its powerful effect on host metabolic and immune homeostasis. Here we summarise the evidence from human studies on the early gut microbiota and paediatric overweight and obesity. Manipulation of the early gut microbiota may represent a promising target for countering the burgeoning metabolic disorders in the paediatric population, provided the assembly patterns of microbiota and their health consequences can be decoded. Therefore, in this review, we pay particular attention to the important ecological drivers affecting the community dynamics of the early gut microbiota. We then discuss the knowledge gaps in commonly studied exposures linking the gut microbiota to metabolic disorders, especially regarding maternal factors and antibiotic use. This review also attempts to give directions for future studies aiming to identify predictive and corrective measures for paediatric metabolic disorders based on the gut microbiota. Gut microbiota; Metabolism; Paediatric overweight and obesity; Ecological driver; Dynamics; Infants.

Interspecies bacterial competition regulates community assembly in the C. elegans intestine

From insects to mammals, a large variety of animals hold in their intestines complex bacterial communities that play an important role in health and disease. To further our understanding of how intestinal bacterial communities assemble and function, we study the C. elegans microbiota with a bottom-up approach by feeding this nematode with bacterial monocultures as well as mixtures of two to eight bacterial species. We find that bacteria colonizing well in monoculture do not always do well in co-cultures due to interspecies bacterial interactions. Moreover, as community diversity increases, the ability to colonize the worm gut in monoculture becomes less important than interspecies interactions for determining community assembly. To explore the role of host-microbe adaptation, we compare bacteria isolated from C. elegans intestines and non-native isolates, and we find that the success of colonization is determined more by a species' taxonomy than by the isolation source. Lastly, by comparing the assembled microbiotas in two C. elegans mutants, we find that innate immunity via the p38 MAPK pathway decreases bacterial abundances yet has little influence on microbiota composition. These results highlight that bacterial interspecies interactions, more so than host-microbe adaptation or gut environmental filtering, play a dominant role in the assembly of the C. elegans microbiota.

Predicted Metabolic Function of the Gut Microbiota of Drosophila melanogaster

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.IMPORTANCE Drosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of Drosophila This revealed that metabolic interactions between Drosophila gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the Drosophila gut and the diversity of metabolites available to influence host traits.

Predicted Metabolic Function of the Gut Microbiota of Drosophila melanogaster

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the Drosophila gut microbiome (from single taxa to the five-member community of Acetobacter and Lactobacillus species) under three nutrient regimes. We show that the metabolic function of Drosophila gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating Drosophila gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.IMPORTANCE Drosophila is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how Drosophila interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of Drosophila This revealed that metabolic interactions between Drosophila gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the Drosophila gut and the diversity of metabolites available to influence host traits.